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Anderson, Joe S.; Williams, Susan K. – Decision Sciences Journal of Innovative Education, 2019
In this project, students asked and attempted to answer questions about themselves by collecting and analyzing data. With the prevalence of big data and business analytics, managers have data and quantitative information available more immediately than ever. However, managers need to understand how to use this information. In this project,…
Descriptors: Data Collection, Data Analysis, Student Projects, Experiential Learning
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Kastberg, David; Murray, Gordon; Ferraro, David; Arieira, Carlos; Roey, Shep; Mamedova, Saida; Liao, Yuqi – National Center for Education Statistics, 2021
The Program for International Student Assessment Young Adult Follow-up Study (PISA YAFS) is a follow-up study with students who participated in PISA 2012 in the United States. The study is designed to measure how performance on PISA 2012 relates to subsequent measures of outcomes and skills of young adults on an online assessment, Education and…
Descriptors: Foreign Countries, Achievement Tests, Secondary School Students, Young Adults
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Choi, Samuel P. M.; Lam, S. S.; Li, Kam Cheong; Wong, Billy T. M. – Educational Technology & Society, 2018
While learning analytics (LA) practices have been shown to be practical and effective, most of them require a huge amount of data and effort. This paper reports a case study which demonstrates the feasibility of practising LA at a low cost for instructors to identify at-risk students in an undergraduate business quantitative methods course.…
Descriptors: Data Collection, Data Analysis, Educational Research, Audience Response Systems
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Beyene, Kassu Mehari; Yimam, Jemal Ayalew – Journal of Education and Practice, 2016
Education is a process by which man transmits his experiences, new findings, and values accumulated over the years, in his struggle for survival and development, through generations. Accordingly, one of the aims of education is to strengthen the individual's and societies' problem solving capacity, ability and culture starting from basic education…
Descriptors: Foreign Countries, Hierarchical Linear Modeling, Academic Achievement, College Students
Arnold, Kimberly E. – ProQuest LLC, 2017
In the 21st century, attainment of a college degree is more important than ever to achieve economic self-sufficiency, employment, and an adequate standard of living. Projections suggest that by 2020, 65% of jobs available in the U.S. will require postsecondary education. This reality creates an unprecedented demand for higher education, and…
Descriptors: Educational Technology, Profiles, Biographies, Demography
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Nokelainen, Petri; Silander, Tomi – Frontline Learning Research, 2014
This commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling. We start by questioning the extremely high classification accuracy with an empirical data from a complex domain. There is…
Descriptors: Models, Classification, Accuracy, Regression (Statistics)
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Baloglu, Nuri – Educational Research and Reviews, 2017
In this study, the effects of family leadership orientation on social entrepreneurship, generativity and academic education success were examined with the views of college students. The study was conducted at a state university in Central Anatolia in Turkey. 402 college students who attending at three different colleges voluntarily participated in…
Descriptors: Leadership, Entrepreneurship, Academic Achievement, College Students
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Newman, David; Newman, Isadore; Hitchcock, John H. – International Journal of Adult Vocational Education and Technology, 2016
The purpose of this article is to inform researchers about and encourage the use of longitudinal designs to further understanding of human resource development and organizational theory. This article presents information about a variety of longitudinal research designs, related statistical procedures, and an overview of general data collecting…
Descriptors: Longitudinal Studies, Organizational Theories, Labor Force Development, Research Design
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Jian, Yuheng Helen; Javaad, Sohail Syed; Golab, Lukasz – Informatics in Education, 2016
In this paper, we take a new look at the problem of analyzing course evaluations. We examine ten years of undergraduate course evaluations from a large Engineering faculty. To the best of our knowledge, our data set is an order of magnitude larger than those used by previous work on this topic, at over 250,000 student evaluations of over 5,000…
Descriptors: Course Evaluation, Undergraduate Students, Engineering Education, Data Collection
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Ifenthaler, Dirk; Schumacher, Clara – Educational Technology Research and Development, 2016
The purpose of this study was to examine student perceptions of privacy principles related to learning analytics. Privacy issues for learning analytics include how personal data are collected and stored as well as how they are analyzed and presented to different stakeholders. A total of 330 university students participated in an exploratory study…
Descriptors: Student Attitudes, Privacy, Data Collection, Data Analysis
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Saelzer, Christine; Lenski, Anna Eva – Journal of Education for Students Placed at Risk, 2016
Truant student behavior can be due to various reasons. Some of these reasons are located in schools. So far, little is known about how student perception of school rules is related to truancy. This study aims to identify types of school attendance policies and how these policies are associated with individual truancy. Self-reports from the German…
Descriptors: Foreign Countries, Secondary School Students, Student Attitudes, Adolescent Attitudes
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Pinder, Jonathan P. – Decision Sciences Journal of Innovative Education, 2014
Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…
Descriptors: Data Collection, Data Analysis, Regression (Statistics), Predictive Measurement
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Lu, Owen H. T.; Huang, Anna Y. Q.; Huang, Jeff C. H.; Lin, Albert J. Q.; Ogata, Hiroaki; Yang, Stephen J. H. – Educational Technology & Society, 2018
Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and is a part of our Precision education used to analyze…
Descriptors: Blended Learning, Educational Technology, Technology Uses in Education, Data Collection
Strecht, Pedro; Cruz, Luís; Soares, Carlos; Mendes-Moreira, João; Abreu, Rui – International Educational Data Mining Society, 2015
Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is…
Descriptors: Comparative Analysis, Classification, Regression (Statistics), Mathematics
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Pampaka, Maria; Hutcheson, Graeme; Williams, Julian – International Journal of Research & Method in Education, 2016
Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…
Descriptors: Data Analysis, Statistical Inference, Error of Measurement, Computation
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